Learning Nonsparse Kernels by Self-Organizing Maps for Structured Data

Self-Organizing Maps for Structured Data

In this paper we are interested in discovering similarities among complex objects which can reasonably be represented by labelled acyclic directed graphs. We show that it is possible to extend the SOM model to study this type of structured inputs. We demonstrate the capabilities of the proposed model by utilizing a relatively large data set taken from an artificial benchmark problem involving v...

"Kernelized" Self-Organizing Maps for Structured Data

The suitability of the well known kernels for trees, and the lesser known SelfOrganizing Map for Structures for categorization tasks on structured data is investigated in this paper. It is shown that a suitable combination of the two approaches, by defining new kernels on the activation map of a Self-Organizing Map for Structures, can result in a system that is significantly more accurate for c...

Learning Metri s for Self-Organizing Maps

Cartogram Data Projection for Self-Organizing Maps

Self-Organizing Maps (SOMs) are often visualized by applying Ultsch’s Unified Distance Matrix (U-Matrix) and labeling the cells of the 2-D grid with training data observations. Although powerful and the de facto standard visualization for SOMs, this does not provide for two key pieces of information when considering real world data mining applications: (a) While the U-Matrix indicates the locat...

Probabilistic self-organizing maps for qualitative data

We present a self-organizing map model to study qualitative data (also called categorical data). It is based on a probabilistic framework which does not assume any prespecified distribution of the input data. Stochastic approximation theory is used to develop a learning rule that builds an approximation of a discrete distribution on each unit. This way, the internal structure of the input datas...